Sensors Integration achieves Six Sigma accuracy in food label inspection with 30% cost reduction
Sensors Integration deployed Computer Vision for Quality Control & Inspection in Food & Beverage. As reported by cognex.com: 6,200,000+ upc codes without fail.
Source-reported figures — cited source: cognex.com
What Sensors Integration was trying to fix
Food and beverage companies face critical safety risks from label mismatches, with the average food recall costing $10 million per incident. Existing inspection systems assumed products were good until failure was detected, leading to potential errors. Manual operator mistakes such as accidentally pressing a teach button could cause days of passing defective products.
What Sensors Integration deployed
Sensors Integration developed the CRIS (Control Reliable Inspection System) using four Cognex DataMan 474 barcode scanners with Edge Intelligence for 360-degree inspection. The system uses a fail-to-safe approach assuming all product is mislabeled until proven correct. RFID-controlled access, time-stamped event logging, and automated line stopping after three consecutive failures provide additional safety layers.
Results
The system achieved accuracy greater than Six Sigma, handling a high volume of UPC codes without a single fail in testing. Costs were reduced by about 30% through control design and multi-reader sync. Using a smaller PLC saved about $10,000, and overall design reduced costs by more than $30,000. Build times and integration periods were shortened.
Key Takeaways
- A fail-to-safe inspection philosophy (assume bad until proven good) provides superior accuracy over traditional approaches
- Multi-reader sync technology can dramatically reduce PLC costs and system complexity
- RFID-controlled access and event logging prevent accidental operator errors that could compromise food safety
Evidence for Sensors Integration's Quality Control & Inspection deployment
- Reported outcome metrics
- 3 cited below
- Cited source
- cognex.com
- Last updated
Explore Related
Vendor
Details
- Industry
- Food & Beverage
- Use Case
- Quality Control & Inspection
- AI Technology
- Computer Vision
- Company Size
- SME
- Company
- Sensors Integration
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